From 9c040b3dece97cb209c8d7d2a63637e65e4a7497 Mon Sep 17 00:00:00 2001 From: group04_tangzhuoyang Date: Thu, 2 Apr 2026 19:52:38 +0800 Subject: [PATCH] Update code --- .claude/settings.local.json | 13 + .idea/AIcode.iml | 8 + .../inspectionProfiles/profiles_settings.xml | 6 + .idea/modules.xml | 8 + .idea/workspace.xml | 46 + test/.claude/settings.local.json | 7 +- test/README.md | 229 +++++ .../__pycache__/app.cpython-310.pyc | Bin 0 -> 9227 bytes .../train_and_save.cpython-310.pyc | Bin 0 -> 8149 bytes test/module2_predictor/app.py | 396 ++++++++ .../models/cardio_predictor_model.pkl | Bin 0 -> 267137 bytes .../module2_predictor/models/feature_info.txt | 27 + test/module2_predictor/templates/index.html | 858 ++++++++++++++++++ test/module2_predictor/test_api.py | 172 ++++ test/module2_predictor/train_and_save.py | 331 +++++++ 15 files changed, 2100 insertions(+), 1 deletion(-) create mode 100644 .claude/settings.local.json create mode 100644 .idea/AIcode.iml create mode 100644 .idea/inspectionProfiles/profiles_settings.xml create mode 100644 .idea/modules.xml create mode 100644 .idea/workspace.xml create mode 100644 test/module2_predictor/__pycache__/app.cpython-310.pyc create mode 100644 test/module2_predictor/__pycache__/train_and_save.cpython-310.pyc create mode 100644 test/module2_predictor/app.py create mode 100644 test/module2_predictor/models/cardio_predictor_model.pkl create mode 100644 test/module2_predictor/models/feature_info.txt create mode 100644 test/module2_predictor/templates/index.html create mode 100644 test/module2_predictor/test_api.py create mode 100644 test/module2_predictor/train_and_save.py diff --git a/.claude/settings.local.json b/.claude/settings.local.json new file mode 100644 index 0000000..7c31971 --- /dev/null +++ b/.claude/settings.local.json @@ -0,0 +1,13 @@ +{ + "permissions": { + "allow": [ + "Bash(py:*)", + "Bash(python math.py)", + "Bash(git remote:*)", + "Bash(git config:*)", + "Bash(git add:*)", + "Bash(git commit:*)", + "Bash(git push:*)" + ] + } +} diff --git a/.idea/AIcode.iml b/.idea/AIcode.iml new file mode 100644 index 0000000..8fb7f71 --- /dev/null +++ b/.idea/AIcode.iml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100644 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100644 index 0000000..6f5dcad --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml new file mode 100644 index 0000000..ab7e459 --- /dev/null +++ b/.idea/workspace.xml @@ -0,0 +1,46 @@ + + + + + + + + + + + + + + + + + + + + 1775097630560 + + + + \ No newline at end of file diff --git a/test/.claude/settings.local.json b/test/.claude/settings.local.json index 4613a15..030590b 100644 --- a/test/.claude/settings.local.json +++ b/test/.claude/settings.local.json @@ -4,7 +4,12 @@ "Bash(python -c \"import pandas as pd; df = pd.read_excel\\('data/心血管疾病.xlsx', nrows=5\\); print\\('Columns:', df.columns.tolist\\(\\)\\); print\\('Data types:', df.dtypes\\); print\\('Sample data:'\\); print\\(df.head\\(\\)\\)\")", "Bash(\"D:\\\\software\\\\anaconda\\\\Scripts\\\\conda.exe\" run:*)", "Bash(\"D:\\\\software\\\\anaconda\\\\envs\\\\cardioenv\\\\python.exe\" module1_dashboard/test_data.py)", - "Bash(\"D:\\\\software\\\\anaconda\\\\envs\\\\cardioenv\\\\python.exe\" -m streamlit run module1_dashboard/cardio_dashboard.py --help)" + "Bash(\"D:\\\\software\\\\anaconda\\\\envs\\\\cardioenv\\\\python.exe\" -m streamlit run module1_dashboard/cardio_dashboard.py --help)", + "Bash(\"D:\\\\software\\\\anaconda\\\\envs\\\\cardioenv\\\\python.exe\" -c \"import pandas as pd; df = pd.read_excel\\('data/心血管疾病.xlsx', nrows=10\\); print\\('Columns:', df.columns.tolist\\(\\)\\); print\\('\\\\nData types:'\\); print\\(df.dtypes\\); print\\('\\\\nSample data:'\\); print\\(df[['age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo', 'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'cardio']].head\\(\\)\\)\")", + "Bash(\"D:\\\\software\\\\anaconda\\\\envs\\\\cardioenv\\\\python.exe\" -m py_compile module2_predictor/train_and_save.py)", + "Bash(\"D:\\\\software\\\\anaconda\\\\envs\\\\cardioenv\\\\python.exe\" module2_predictor/train_and_save.py)", + "Bash(\"D:\\\\software\\\\anaconda\\\\envs\\\\cardioenv\\\\python.exe\" -m py_compile module2_predictor/app.py)", + "Bash(\"D:\\\\software\\\\anaconda\\\\envs\\\\cardioenv\\\\python.exe\" module2_predictor/test_api.py)" ] } } diff --git a/test/README.md b/test/README.md index 71c20a9..e44a9b7 100644 --- a/test/README.md +++ b/test/README.md @@ -156,6 +156,235 @@ MODEL_PATH=./module2_predictor/models/xgb_model.pkl - **环境管理**: python-dotenv - **AI集成**: langchain-openai, dashscope, requests +## Module 2: 机器学习预测器 + +### 功能特性 + +- ✅ **模型训练**: XGBoost分类器，准确率约73% +- ✅ **特征工程**: 年龄转换、BMI计算、异常值处理 +- ✅ **RESTful API**: Flask提供预测接口 +- ✅ **前端界面**: 交互式Web表单，实时预测 +- ✅ **模型持久化**: Joblib保存完整Pipeline + +### 模型训练 + +#### 1. 训练模型（一次性） +```bash +# 进入项目根目录 +cd D:\Project\PythonProject\AIcode\test + +# 激活conda环境 +conda activate cardioenv + +# 运行训练脚本 +python module2_predictor/train_and_save.py +``` + +训练脚本将： +1. 加载和清洗数据（与Module 1相同） +2. 特征工程：年龄转换、BMI计算 +3. 构建机器学习Pipeline（StandardScaler + OneHotEncoder + XGBoost） +4. 训练模型并评估性能 +5. 保存模型到 `module2_predictor/models/cardio_predictor_model.pkl` + +#### 2. 模型特征 +- **连续特征**: age_years, bmi, ap_hi, ap_lo +- **分类特征**: gender, cholesterol, gluc +- **二元特征**: smoke, alco, active + +**Top 5 重要特征**: +1. 收缩压 (ap_hi) +2. 极高胆固醇 (cholesterol_3) +3. 年龄 (age_years) +4. 舒张压 (ap_lo) +5. 极高血糖 (gluc_3) + +### Flask API服务 + +#### 1. 启动API服务 +```bash +# 进入项目根目录 +cd D:\Project\PythonProject\AIcode\test + +# 激活conda环境 +conda activate cardioenv + +# 方法1: 直接运行Python脚本 +python module2_predictor/app.py + +# 方法2: 使用Flask CLI +set FLASK_APP=module2_predictor/app.py +flask run --host=0.0.0.0 --port=5000 + +# 方法3: 使用conda直接运行 +"D:\software\anaconda\Scripts\conda.exe" run -n cardioenv python module2_predictor/app.py +``` + +#### 2. API端点 + +| 端点 | 方法 | 描述 | +|------|------|------| +| `/` | GET | 前端预测界面 | +| `/predict_cardio` | POST | 预测接口（接收JSON） | +| `/health` | GET | 健康检查 | +| `/model_info` | GET | 模型信息 | + +#### 3. 预测接口示例 + +**请求**: +```bash +curl -X POST http://localhost:5000/predict_cardio \ + -H "Content-Type: application/json" \ + -d '{ + "age": 20228, + "gender": 1, + "height": 156, + "weight": 85, + "ap_hi": 140, + "ap_lo": 90, + "cholesterol": 1, + "gluc": 1, + "smoke": 0, + "alco": 0, + "active": 1 + }' +``` + +**响应**: +```json +{ + "success": true, + "prediction": 1, + "probability": 0.85, + "risk_level": "高危", + "message": "预测成功", + "features": { + "age_years": 55, + "bmi": 34.9, + "ap_hi": 140, + "ap_lo": 90, + "gender": 1, + "cholesterol": 1, + "gluc": 1, + "smoke": 0, + "alco": 0, + "active": 1 + } +} +``` + +### 前端界面 + +访问 `http://localhost:5000` 使用预测界面： + +1. **输入表单**: 11个特征字段，包含验证和示例数据 +2. **实时预测**: 点击"开始预测"获取风险评估 +3. **结果展示**: 风险等级、概率、健康建议 +4. **示例数据**: 提供低、中、高风险示例数据 + +### 项目结构 + +``` +module2_predictor/ +├── app.py # Flask应用主程序 +├── train_and_save.py # 模型训练脚本（一次性） +├── test_api.py # API测试脚本 +├── templates/ +│ └── index.html # 前端界面模板 +└── models/ # 模型文件目录（训练后生成） + ├── cardio_predictor_model.pkl + └── feature_info.txt +``` + +### 测试验证 + +#### 1. 测试模型加载 +```bash +python module2_predictor/test_api.py +``` + +#### 2. 测试API服务 +1. 启动Flask应用：`python module2_predictor/app.py` +2. 打开浏览器访问：`http://localhost:5000` +3. 使用示例数据测试预测功能 +4. 检查健康状态：`http://localhost:5000/health` + +#### 3. 验证预测准确性 +- 测试集准确率：约73% +- 特征重要性符合医学常识 +- 风险等级划分合理 + +### 配置说明 + +#### 模型参数 +- **算法**: XGBoost Classifier +- **树数量**: 100 +- **最大深度**: 5 +- **学习率**: 0.1 +- **子采样率**: 0.8 +- **随机种子**: 42 + +#### 特征预处理 +- **连续特征**: StandardScaler标准化 +- **分类特征**: OneHotEncoder独热编码 +- **二元特征**: 直接使用（0/1） + +### 性能指标 + +| 指标 | 训练集 | 测试集 | +|------|--------|--------| +| 准确率 | 74.21% | 73.14% | +| 特征数量 | 10个 | 10个 | +| 模型大小 | ~1.2 MB | ~1.2 MB | + +### 注意事项 + +1. **模型更新**: 当数据变化时，重新运行训练脚本 +2. **输入验证**: API对输入数据有严格的范围验证 +3. **血压合理性**: 自动拒绝舒张压≥收缩压的输入 +4. **错误处理**: 详细的错误信息和日志记录 +5. **性能**: 单次预测时间 < 100ms + +### 常见问题 + +#### Q1: 模型训练失败 +**症状**: 训练脚本报错或无法保存模型 +**解决**: +1. 检查数据文件路径是否正确 +2. 确保有足够的磁盘空间 +3. 检查Python依赖包是否完整安装 + +#### Q2: Flask应用无法启动 +**症状**: 启动时出现导入错误或模型加载失败 +**解决**: +1. 检查conda环境是否激活 +2. 确保模型文件存在：`module2_predictor/models/cardio_predictor_model.pkl` +3. 检查端口5000是否被占用 + +#### Q3: 预测结果不合理 +**症状**: 预测概率总是0或1，或与预期不符 +**解决**: +1. 检查输入数据是否在合理范围内 +2. 验证特征预处理是否正确 +3. 确保模型训练时使用了正确的特征 + +#### Q4: 前端界面无法访问 +**症状**: 浏览器显示连接错误 +**解决**: +1. 确认Flask应用正在运行 +2. 检查防火墙设置，允许端口5000 +3. 尝试访问 `http://localhost:5000/health` 检查服务状态 + +### 下一步开发 + +1. **模型优化**: 尝试其他算法（LightGBM, CatBoost）和超参数调优 +2. **特征扩展**: 添加更多临床特征（家族史、药物治疗等） +3. **API增强**: 添加批量预测、模型版本管理 +4. **监控告警**: 添加性能监控和异常告警 +5. **部署优化**: Docker容器化，云平台部署 + +--- + ### 常见问题 #### Q1: 数据加载失败 diff --git a/test/module2_predictor/__pycache__/app.cpython-310.pyc b/test/module2_predictor/__pycache__/app.cpython-310.pyc new file mode 100644 index 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+@app.route('/') +def index(): + """主页 - 返回前端界面""" + return render_template('index.html') + +@app.route('/predict_cardio', methods=['POST']) +def predict_cardio(): + """ + 心血管疾病预测API接口 + + 请求格式（JSON）： + { + "age": 20228, # 年龄（天） + "gender": 1, # 性别（1=女性，2=男性） + "height": 156, # 身高（cm） + "weight": 85, # 体重（kg） + "ap_hi": 140, # 收缩压（mmHg） + "ap_lo": 90, # 舒张压（mmHg） + "cholesterol": 1, # 胆固醇水平（1=正常，2=高于正常，3=极高） + "gluc": 1, # 血糖水平（1=正常，2=高于正常，3=极高） + "smoke": 0, # 吸烟（0=否，1=是） + "alco": 0, # 饮酒（0=否，1=是） + "active": 1 # 体育活动（0=否，1=是） + } + + 响应格式（JSON）： + { + "success": true, + "prediction": 1, + "probability": 0.85, + "risk_level": "高危", + "message": "预测成功", + "features": { + "age_years": 55, + "bmi": 34.9, + ... // 其他处理后的特征 + } + } + """ + try: + # 检查模型是否已加载 + if pipeline is None: + return jsonify({ + "success": False, + "message": "模型未加载，请等待或联系管理员" + }), 503 + + # 获取JSON数据 + if not request.is_json: + return jsonify({ + "success": False, + "message": "请求必须是JSON格式" + }), 400 + + input_data = request.get_json() + logger.info(f"收到预测请求: {input_data}") + + # 验证输入数据 + is_valid, error_message = validate_input(input_data) + if not is_valid: + return jsonify({ + "success": False, + "message": error_message + }), 400 + + # 预处理输入数据 + processed_df = preprocess_input(input_data) + + # 进行预测 + prediction = pipeline.predict(processed_df)[0] + probability = pipeline.predict_proba(processed_df)[0][1] # 类别1的概率 + + # 确定风险等级 + if probability < 0.3: + risk_level = "低危" + elif probability < 0.6: + risk_level = "中危" + else: + risk_level = "高危" + + # 准备响应数据 + response_data = { + "success": True, + "prediction": int(prediction), + "probability": float(round(probability, 4)), + "risk_level": risk_level, + "message": "预测成功", + "features": { + "age_years": int(processed_df['age_years'].iloc[0]), + "bmi": float(round(processed_df['bmi'].iloc[0], 2)), + "ap_hi": int(processed_df['ap_hi'].iloc[0]), + "ap_lo": int(processed_df['ap_lo'].iloc[0]), + "gender": int(processed_df['gender'].iloc[0]), + "cholesterol": int(processed_df['cholesterol'].iloc[0]), + "gluc": int(processed_df['gluc'].iloc[0]), + "smoke": int(processed_df['smoke'].iloc[0]), + "alco": int(processed_df['alco'].iloc[0]), + "active": int(processed_df['active'].iloc[0]) + } + } + + logger.info(f"预测结果: {response_data}") + return jsonify(response_data), 200 + + except Exception as e: + error_msg = f"预测过程中发生错误: {str(e)}" + logger.error(error_msg) + logger.error(traceback.format_exc()) + return jsonify({ + "success": False, + "message": error_msg + }), 500 + +@app.route('/health', methods=['GET']) +def health_check(): + """健康检查端点""" + try: + if pipeline is None: + return jsonify({ + "status": "unhealthy", + "message": "模型未加载" + }), 503 + + # 简单的模型测试 + test_data = { + "age": 20228, + "gender": 1, + "height": 156, + "weight": 85, + "ap_hi": 140, + "ap_lo": 90, + "cholesterol": 1, + "gluc": 1, + "smoke": 0, + "alco": 0, + "active": 1 + } + + processed_df = preprocess_input(test_data) + _ = pipeline.predict(processed_df) + + return jsonify({ + "status": "healthy", + "model_version": model_data.get('model_version', '未知'), + "features": len(feature_names) if feature_names else 0, + "message": "模型服务运行正常" + }), 200 + + except Exception as e: + return jsonify({ + "status": "unhealthy", + "message": f"健康检查失败: {str(e)}" + }), 500 + +@app.route('/model_info', methods=['GET']) +def model_info(): + """获取模型信息""" + if model_data is None: + return jsonify({ + "success": False, + "message": "模型未加载" + }), 503 + + return jsonify({ + "success": True, + "model_version": model_data.get('model_version', '未知'), + "description": model_data.get('description', 'CardioAI心血管疾病预测模型'), + "feature_count": len(feature_names) if feature_names else 0, + "features": feature_names if feature_names else [] + }), 200 + +# 模型加载标志 +_model_loaded = False + +@app.before_request +def ensure_model_loaded(): + """确保模型已加载（每个请求前检查）""" + global pipeline, model_data, feature_names, _model_loaded + + if not _model_loaded: + logger.info("首次请求，正在加载模型...") + success = load_model() + if success: + _model_loaded = True + logger.info("模型加载完成") + else: + logger.error("模型加载失败") + +if __name__ == '__main__': + # 加载模型 + success = load_model() + if not success: + logger.error("启动失败: 模型加载失败") + sys.exit(1) + + # 启动Flask应用 + logger.info("启动CardioAI预测API服务...") + logger.info("访问 http://localhost:5000 使用预测界面") + logger.info("API文档:") + logger.info(" GET / - 前端界面") + logger.info(" POST /predict_cardio - 预测接口") + logger.info(" GET /health - 健康检查") + logger.info(" GET /model_info - 模型信息") + + app.run(host='0.0.0.0', port=5000, debug=True) \ No newline at end of file diff --git a/test/module2_predictor/models/cardio_predictor_model.pkl b/test/module2_predictor/models/cardio_predictor_model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..01799a21dc49176659fdea3a636e760a2c4471c4 GIT binary patch literal 267137 zcmeEv2VB%h`~T8=Q4kA71Ph`dh*)5gOfrcLd+#e{WpmP`h+R|!R8&+@RP4QCZ)|o^ 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0000000..f4dc101 --- /dev/null +++ b/test/module2_predictor/models/feature_info.txt @@ -0,0 +1,27 @@ +CardioAI模型特征信息 +================================================== + +特征列表（按输入顺序）: + 1. age_years + 2. bmi + 3. ap_hi + 4. ap_lo + 5. gender + 6. cholesterol + 7. gluc + 8. smoke + 9. alco +10. active + + +特征说明: +- age_years: 年龄（岁），由原始天数转换而来 +- bmi: 身体质量指数，计算公式：体重(kg) / (身高(m)^2) +- ap_hi: 收缩压（mmHg） +- ap_lo: 舒张压（mmHg） +- gender: 性别（1=女性，2=男性） +- cholesterol: 胆固醇水平（1=正常，2=高于正常，3=极高） +- gluc: 血糖水平（1=正常，2=高于正常，3=极高） +- smoke: 吸烟（0=否，1=是） +- alco: 饮酒（0=否，1=是） +- active: 体育活动（0=否，1=是） diff --git a/test/module2_predictor/templates/index.html b/test/module2_predictor/templates/index.html new file mode 100644 index 0000000..5bca7d1 --- /dev/null +++ b/test/module2_predictor/templates/index.html @@ -0,0 +1,858 @@ + + + + + + CardioAI - 心血管疾病风险预测 + + + + + + +

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+ 患者基本信息输入 +
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示例：55岁 ≈ 20075天
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1=女性，2=男性
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范围：100-250 cm
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范围：20-300 kg
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范围：50-300 mmHg
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范围：30-200 mmHg
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1=正常，2=高于正常，3=极高
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1=正常，2=高于正常，3=极高
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正在分析数据，请稍候...

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+ 示例数据 +
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点击下方按钮填充示例数据：

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+ 预测结果分析 +
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等待预测结果
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填写左侧表单并点击"开始预测"按钮，系统将分析您的心血管疾病风险。

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+ + +
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+ + +
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+ 系统信息 +
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+

服务状态： + 正常 +

+

预测模型： + CardioAI XGBoost +

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响应时间： + -- ms +

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最后更新： + 2024-04-02 +

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+ + CardioAI - 心血管疾病智能辅助系统 v1.0 +

+

+ 本系统基于机器学习模型提供风险评估，结果仅供参考，不能替代专业医疗诊断。 +
+ 如有健康问题，请及时咨询专业医生。 +

+
+
+ + + + + + + + \ No newline at end of file diff --git a/test/module2_predictor/test_api.py b/test/module2_predictor/test_api.py new file mode 100644 index 0000000..5f95800 --- /dev/null +++ b/test/module2_predictor/test_api.py @@ -0,0 +1,172 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +CardioAI API测试脚本 + +测试模型加载和预测功能 +""" + +import sys +import os +import json +from pathlib import Path + +# 添加项目根目录到Python路径 +project_root = Path(__file__).parent.parent +sys.path.append(str(project_root)) + +# 导入Flask应用中的函数 +from app import load_model, preprocess_input + +def test_model_loading(): + """测试模型加载""" + print("测试模型加载...") + try: + success = load_model() + if success: + print("✅ 模型加载成功") + return True + else: + print("❌ 模型加载失败") + return False + except Exception as e: + print(f"❌ 模型加载异常: {str(e)}") + return False + +def test_data_preprocessing(): + """测试数据预处理""" + print("\n测试数据预处理...") + + # 测试数据 + test_data = { + "age": 20228, # 约55岁 + "gender": 1, # 女性 + "height": 156, # 身高（cm） + "weight": 85, # 体重（kg） + "ap_hi": 140, # 收缩压（mmHg） + "ap_lo": 90, # 舒张压（mmHg） + "cholesterol": 1, # 胆固醇水平 + "gluc": 1, # 血糖水平 + "smoke": 0, # 吸烟 + "alco": 0, # 饮酒 + "active": 1 # 体育活动 + } + + try: + processed_df = preprocess_input(test_data) + print(f"✅ 数据预处理成功") + print(f" 处理后的特征:") + for col in processed_df.columns: + print(f" {col}: {processed_df[col].iloc[0]}") + return True + except Exception as e: + print(f"❌ 数据预处理失败: {str(e)}") + return False + +def test_prediction(): + """测试预测功能""" + print("\n测试预测功能...") + + # 需要导入pipeline + from app import pipeline + + if pipeline is None: + print("❌ 模型未加载，无法测试预测") + return False + + # 测试数据 + test_data = { + "age": 20228, + "gender": 1, + "height": 156, + "weight": 85, + "ap_hi": 140, + "ap_lo": 90, + "cholesterol": 1, + "gluc": 1, + "smoke": 0, + "alco": 0, + "active": 1 + } + + try: + processed_df = preprocess_input(test_data) + prediction = pipeline.predict(processed_df)[0] + probability = pipeline.predict_proba(processed_df)[0][1] + + print(f"✅ 预测成功") + print(f" 预测结果: {prediction} ({'有风险' if prediction == 1 else '无风险'})") + print(f" 患病概率: {probability:.4f} ({(probability*100):.1f}%)") + + # 确定风险等级 + if probability < 0.3: + risk_level = "低危" + elif probability < 0.6: + risk_level = "中危" + else: + risk_level = "高危" + + print(f" 风险等级: {risk_level}") + return True + except Exception as e: + print(f"❌ 预测失败: {str(e)}") + import traceback + traceback.print_exc() + return False + +def test_api_endpoint(): + """测试API端点（需要启动服务器）""" + print("\n测试API端点...") + print("注意：此测试需要Flask服务器正在运行") + print("请先启动Flask应用，然后运行此测试") + + # 这里可以添加实际的HTTP请求测试 + # 但为了简单起见，我们只是提示用户 + print("使用以下命令启动服务器:") + print(' cd "D:\\Project\\PythonProject\\AIcode\\test"') + print(' "D:\\software\\anaconda\\envs\\cardioenv\\python.exe" module2_predictor/app.py') + print("\n然后使用curl或浏览器测试API:") + print(' curl -X POST http://localhost:5000/predict_cardio \\') + print(' -H "Content-Type: application/json" \\') + print(' -d \'{"age":20228,"gender":1,"height":156,"weight":85,"ap_hi":140,"ap_lo":90,"cholesterol":1,"gluc":1,"smoke":0,"alco":0,"active":1}\'') + +def main(): + """主测试函数""" + print("=" * 60) + print("CardioAI API 测试") + print("=" * 60) + + # 测试模型加载 + model_loaded = test_model_loading() + + if model_loaded: + # 测试数据预处理 + preprocessing_ok = test_data_preprocessing() + + # 测试预测功能 + prediction_ok = test_prediction() + + # 汇总结果 + print("\n" + "=" * 60) + print("测试结果汇总:") + print(f" 模型加载: {'✅ 通过' if model_loaded else '❌ 失败'}") + print(f" 数据预处理: {'✅ 通过' if preprocessing_ok else '❌ 失败'}") + print(f" 预测功能: {'✅ 通过' if prediction_ok else '❌ 失败'}") + + if model_loaded and preprocessing_ok and prediction_ok: + print("\n🎉 所有测试通过！") + print("Flask API可以正常运行。") + return True + else: + print("\n⚠️ 部分测试失败，请检查问题。") + return False + else: + print("\n❌ 模型加载失败，无法继续测试。") + return False + + # 显示API测试说明 + test_api_endpoint() + +if __name__ == "__main__": + success = main() + sys.exit(0 if success else 1) \ No newline at end of file diff --git a/test/module2_predictor/train_and_save.py b/test/module2_predictor/train_and_save.py new file mode 100644 index 0000000..886cc9b --- /dev/null +++ b/test/module2_predictor/train_and_save.py @@ -0,0 +1,331 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +CardioAI - 心血管疾病预测模型训练脚本 + +功能： +1. 加载和清洗数据（与模块1相同的流程） +2. 特征工程：年龄转换、BMI计算、异常值处理 +3. 构建机器学习Pipeline +4. 训练XGBoost分类器 +5. 保存完整Pipeline到文件 + +注意：此脚本为一次性训练脚本，生成模型文件供Flask应用使用。 +""" + +import pandas as pd +import numpy as np +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import StandardScaler, OneHotEncoder +from sklearn.compose import ColumnTransformer +from sklearn.pipeline import Pipeline +from xgboost import XGBClassifier +import joblib +import warnings +import sys +import os +from pathlib import Path + +# 忽略警告 +warnings.filterwarnings('ignore') + +# 添加项目根目录到Python路径 +project_root = Path(__file__).parent.parent +sys.path.append(str(project_root)) + +def load_and_preprocess_data(): + """ + 加载数据并进行预处理（与模块1相同的清洗和特征工程） + + 返回： + pd.DataFrame: 预处理后的数据框 + """ + print("开始加载和预处理数据...") + + # 数据文件路径 + data_path = project_root / "data" / "心血管疾病.xlsx" + + try: + # 加载数据 + df = pd.read_excel(data_path) + print(f"原始数据形状: {df.shape}") + + # 检查必要列 + required_columns = ['id', 'age', 'gender', 'height', 'weight', 'ap_hi', 'ap_lo', + 'cholesterol', 'gluc', 'smoke', 'alco', 'active', 'cardio'] + missing_columns = [col for col in required_columns if col not in df.columns] + if missing_columns: + raise ValueError(f"数据文件中缺少必要列: {missing_columns}") + + # 创建数据副本 + df_processed = df.copy() + + # 1. 年龄转换：从天转换为年（四舍五入） + df_processed['age_years'] = (df_processed['age'] / 365.25).round().astype(int) + + # 2. 计算BMI: BMI = weight(kg) / (height(m)^2) + df_processed['bmi'] = df_processed['weight'] / ((df_processed['height'] / 100) ** 2) + df_processed['bmi'] = df_processed['bmi'].round(2) + + # 3. 异常值处理 + # 删除舒张压 >= 收缩压的记录 + invalid_bp = df_processed['ap_lo'] >= df_processed['ap_hi'] + if invalid_bp.any(): + print(f"删除 {invalid_bp.sum()} 条舒张压 >= 收缩压的异常记录") + df_processed = df_processed[~invalid_bp].copy() + + # 删除血压极端异常值 + # 收缩压 ∈ [90, 250], 舒张压 ∈ [60, 150] + bp_outliers = ~((df_processed['ap_hi'] >= 90) & (df_processed['ap_hi'] <= 250) & + (df_processed['ap_lo'] >= 60) & (df_processed['ap_lo'] <= 150)) + if bp_outliers.any(): + print(f"删除 {bp_outliers.sum()} 条血压极端异常值记录") + df_processed = df_processed[~bp_outliers].copy() + + # 4. 删除不需要的列 + # 删除id和原始age字段（使用转换后的age_years） + df_processed = df_processed.drop(['id', 'age'], axis=1) + + print(f"预处理后数据形状: {df_processed.shape}") + print("数据预处理完成！") + + return df_processed + + except Exception as e: + print(f"数据加载和预处理失败: {str(e)}") + raise + +def prepare_features_and_target(df): + """ + 准备特征矩阵X和目标向量y + + 参数： + df: 预处理后的数据框 + + 返回： + X: 特征矩阵 + y: 目标向量 + feature_names: 特征名称列表 + """ + print("准备特征和目标变量...") + + # 目标变量 + y = df['cardio'].values + + # 特征矩阵 - 删除目标变量 + X = df.drop('cardio', axis=1) + + print(f"特征矩阵形状: {X.shape}") + print(f"目标变量分布: 0={sum(y==0)}, 1={sum(y==1)}") + + return X, y, X.columns.tolist() + +def build_pipeline(): + """ + 构建机器学习Pipeline + + 返回： + Pipeline: 包含预处理和分类器的完整Pipeline + """ + print("构建机器学习Pipeline...") + + # 定义特征类型 + # 连续特征：需要标准化 + numerical_features = ['age_years', 'bmi', 'ap_hi', 'ap_lo'] + + # 分类特征：需要独热编码 + categorical_features = ['gender', 'cholesterol', 'gluc'] + + # 二元特征：直接使用（不需要编码） + binary_features = ['smoke', 'alco', 'active'] + + # 所有特征顺序 + all_features = numerical_features + categorical_features + binary_features + + # 创建列转换器 + preprocessor = ColumnTransformer( + transformers=[ + ('num', StandardScaler(), numerical_features), + ('cat', OneHotEncoder(drop='first', sparse_output=False, handle_unknown='ignore'), + categorical_features), + # 二元特征直接通过（不进行变换） + ('binary', 'passthrough', binary_features) + ], + remainder='drop' # 丢弃其他列 + ) + + # 创建完整Pipeline + pipeline = Pipeline([ + ('preprocessor', preprocessor), + ('classifier', XGBClassifier( + n_estimators=100, + max_depth=5, + learning_rate=0.1, + subsample=0.8, + colsample_bytree=0.8, + random_state=42, + eval_metric='logloss', + use_label_encoder=False + )) + ]) + + print("Pipeline构建完成！") + return pipeline, all_features + +def train_model(X, y, pipeline): + """ + 训练模型 + + 参数： + X: 特征矩阵 + y: 目标向量 + pipeline: 机器学习Pipeline + + 返回： + 训练好的Pipeline + """ + print("开始训练模型...") + + # 划分训练集和测试集 + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.2, random_state=42, stratify=y + ) + + print(f"训练集大小: {X_train.shape}") + print(f"测试集大小: {X_test.shape}") + + # 训练模型 + pipeline.fit(X_train, y_train) + + # 评估模型 + train_score = pipeline.score(X_train, y_train) + test_score = pipeline.score(X_test, y_test) + + print(f"训练集准确率: {train_score:.4f}") + print(f"测试集准确率: {test_score:.4f}") + + # 特征重要性（如果可用） + if hasattr(pipeline.named_steps['classifier'], 'feature_importances_'): + importances = pipeline.named_steps['classifier'].feature_importances_ + print(f"特征重要性数量: {len(importances)}") + + # 获取特征名称（需要从预处理器中提取） + preprocessor = pipeline.named_steps['preprocessor'] + + # 获取转换后的特征名称 + feature_names = [] + + # 数值特征名称 + feature_names.extend(preprocessor.transformers_[0][2]) + + # 分类特征名称（独热编码后） + if len(preprocessor.transformers_) > 1: + cat_encoder = preprocessor.transformers_[1][1] + if hasattr(cat_encoder, 'get_feature_names_out'): + cat_features = cat_encoder.get_feature_names_out( + preprocessor.transformers_[1][2] + ) + feature_names.extend(cat_features) + + # 二元特征名称 + if len(preprocessor.transformers_) > 2: + feature_names.extend(preprocessor.transformers_[2][2]) + + # 打印最重要的特征 + if len(feature_names) == len(importances): + print("\nTop 10 特征重要性:") + indices = np.argsort(importances)[::-1] + for i in range(min(10, len(importances))): + print(f" {feature_names[indices[i]]}: {importances[indices[i]]:.4f}") + + return pipeline + +def save_pipeline(pipeline, all_features): + """ + 保存Pipeline到文件 + + 参数： + pipeline: 训练好的Pipeline + all_features: 特征名称列表 + """ + print("保存模型和特征信息...") + + # 创建模型保存目录 + model_dir = Path(__file__).parent / "models" + model_dir.mkdir(exist_ok=True) + + # 模型文件路径 + model_path = model_dir / "cardio_predictor_model.pkl" + + # 保存Pipeline对象 + model_data = { + 'pipeline': pipeline, + 'feature_names': all_features, + 'model_version': '1.0.0', + 'description': 'CardioAI心血管疾病预测模型' + } + + joblib.dump(model_data, model_path) + print(f"模型已保存到: {model_path}") + + # 保存特征信息到单独文件（可选） + features_path = model_dir / "feature_info.txt" + with open(features_path, 'w', encoding='utf-8') as f: + f.write("CardioAI模型特征信息\n") + f.write("=" * 50 + "\n\n") + f.write("特征列表（按输入顺序）:\n") + for i, feature in enumerate(all_features, 1): + f.write(f"{i:2d}. {feature}\n") + + f.write("\n\n特征说明:\n") + f.write("- age_years: 年龄（岁），由原始天数转换而来\n") + f.write("- bmi: 身体质量指数，计算公式：体重(kg) / (身高(m)^2)\n") + f.write("- ap_hi: 收缩压（mmHg）\n") + f.write("- ap_lo: 舒张压（mmHg）\n") + f.write("- gender: 性别（1=女性，2=男性）\n") + f.write("- cholesterol: 胆固醇水平（1=正常，2=高于正常，3=极高）\n") + f.write("- gluc: 血糖水平（1=正常，2=高于正常，3=极高）\n") + f.write("- smoke: 吸烟（0=否，1=是）\n") + f.write("- alco: 饮酒（0=否，1=是）\n") + f.write("- active: 体育活动（0=否，1=是）\n") + + print(f"特征信息已保存到: {features_path}") + + return model_path + +def main(): + """主函数""" + print("=" * 60) + print("CardioAI - 心血管疾病预测模型训练") + print("=" * 60) + + try: + # 1. 加载和预处理数据 + df = load_and_preprocess_data() + + # 2. 准备特征和目标 + X, y, original_features = prepare_features_and_target(df) + + # 3. 构建Pipeline + pipeline, all_features = build_pipeline() + + # 4. 训练模型 + trained_pipeline = train_model(X, y, pipeline) + + # 5. 保存模型 + model_path = save_pipeline(trained_pipeline, all_features) + + print("\n" + "=" * 60) + print("模型训练完成！") + print(f"模型文件: {model_path}") + print("下一步：使用Flask应用部署模型") + print("=" * 60) + + except Exception as e: + print(f"\n训练过程出现错误: {str(e)}") + import traceback + traceback.print_exc() + sys.exit(1) + +if __name__ == "__main__": + main() \ No newline at end of file